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30 Agents Every AI Engineer Must Build

30 Agents Every AI Engineer Must Build

By : Imran Ahmad
4.5 (2)
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30 Agents Every AI Engineer Must Build

30 Agents Every AI Engineer Must Build

4.5 (2)
By: Imran Ahmad

Overview of this book

As AI evolves from passive tools into proactive collaborators, intelligent agents are leading a fundamental shift in computing. This guide provides the critical knowledge of agent architectures, practical tools, and industry approaches needed to build robust, autonomous AI systems that do more than just generate text—they act. You will begin by mastering foundational capabilities: perception, memory, reasoning, planning, and learning. You’ll gain deep insight into the cognitive loops that drive autonomous behavior and build sophisticated architectures using frameworks such as LangChain and LangGraph. The book explores high-impact applications across diverse sectors, including software development, finance, manufacturing, legal and education, to show how agents optimize workflows, automate quality control, and enhance advisory systems. Through real-world case studies, you will create agents capable of contextual reasoning, effective tool use, and seamless human collaboration. Finally, you’ll learn essential strategies for deployment, management, and ethical alignment, ensuring your AI solutions are both scalable and responsible in production environments. Whether you're building your first intelligent agent or improving business systems, this book provides clear, actionable guidance for creating scalable and responsible AI solutions. *Email sign-up and proof of purchase required
Table of Contents (19 chapters)
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18
Index

The General Problem Solver

One of the most ambitious designs in artificial intelligence is the General Problem Solver, which unlike specialized agents that are confined to narrow domains, is designed to reason broadly, transfer knowledge between contexts, and develop new strategies for problems it has never encountered before. Its purpose is not to achieve optimal performance in one specific area but to establish a foundation for generalized reasoning and flexible problem-solving.

A General Problem Solver operates on the principle of meta-reasoning, or the ability to reason about its own reasoning process. Instead of following a fixed set of rules, it evaluates its approach dynamically, identifies gaps in logic or knowledge, and refines its strategy. For instance, when faced with a novel engineering or diagnostic problem, the General Problem Solver agent decomposes it into smaller subgoals, chooses suitable reasoning or learning techniques, and iteratively updates its plan based...

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30 Agents Every AI Engineer Must Build
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